Current methodologies for identification of many diseased states rely on manual visual interpretations of fixed histological samples from target tissues. Such methodologies include, but are not limited to, microscopic image analysis of tumor morphology as well as immunohistochemistry that help disease state classification. Prediction of disease aggressiveness and outcome facilitate selective employment of better therapeutic options. For example, in case of estrogen receptor-positive (ER+) breast cancer (BCa) patients, identifying which patients will benefit from adjuvant chemotherapy over standard hormonal therapy would help limit the use of chemotherapy to more aggressive forms of breast cancer.
Recent advances in genomics and proteomics have led to improvements in diagnostic and prognostic methods utilizing changes in patterns of gene and protein expression profiles. For example, prognosis and treatment of early stage ER+BCa are often guided by the Oncotype DX™ genomic assay (Genomic Health, Inc.), which ascertains a Recurrence Score (RS) correlated with likelihood of recurrence. (Paik, S. et al., “A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.” N. Engl. J. Med., December 2004, 351(27): 2817-2826). WO 2011/044513 (“Diagnostic and Prognostic Markers of Cancer”) describes a method for determining risk of recurrent breast cancer and WO 2011/005570 (“Systems and methods for treating, diagnosing and predicting the response to therapy of breast cancer”) describes methods of assessing the aggressiveness of breast cancer in a subject and for determining whether a patient will derive benefit from a given treatment regimen, as measured by protein expression levels of specific biomarkers for breast cancer. However, such molecular-based assays may have limited value in accomplishing any additional predictive power over standard histological analyses of disease tissue samples, for example, in ascertaining grading and target receptor status in breast cancer patients. (Weigelt, B. and Reis-Filho, J. S., “Molecular profiling currently offers no more than tumor morphology and basic immunohistochemistry.” Br. Can. Res., 2010, 12(Suppl 4):S5).
Visual analysis of tumor grade in BCa histopathology has shown significant value in predicting patient outcome. (Bloom, H. J. et al., “Histological grading and prognosis in breast cancer; a study of 1409 cases of which 359 have been followed for 15 years.” Br. J. Cancer, September 1957, 11(3): 359-377). Nevertheless, such methods require specialized equipment, significant time to obtain results, are expensive, and the results obtained may have limited reproducibility due to high inter- and intra-clinician variability.
Manual image analysis techniques entail implicit partitioning of an entire histopathology slide into many fields-of-view (FOVs) and incorporating image features from each FOV to arrive at a diagnostic decision for the entire slide. Computerized approaches to whole-slide classification involve extraction of image features for purposes of training of a classifier from within empirically selected FOVs. (Sertel, O. et al., “Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development.”, Pattern Recognit., June 2000, 42(6): 1093-1103, 7). The empirical selection of FOVs for computerized analysis of histopathology slides presents two main concerns. First, it is a subjective and time-consuming task that requires manual intervention by an expert, an issue that would impede the development of a truly automated classification system. Second, many diseases such as BCa, are known to contain intratumoral heterogeneity (Torres, L. et al., “Intratumor genomic heterogeneity in breast cancer with clonal divergence between primary carcinomas and lymph node metastases”, Breast Cancer Res. Treat., April 2007, 102(2):143-155). Thus, different types of cancer (e.g. ductal carcinoma in situ and invasive ductal cancer) and levels of malignancy (e.g. low and intermediate grades) may be present in a single histopathology slide, giving rise to irreproducibility of results. For example, the CD34 protein is a popular indicator of angiogenesis and, hence, tumor growth and metastasis (Weidner, N. et al., “Tumor angiogenesis and metastasis-correlation in invasive breast carcinoma.”, N. Engl. J. Med., January 1991, 324(1):1-8). Previously, both qualitative and quantitative assessments of immunohistochemically (IHC) stained slides with CD34 have characterized IHC staining via “hotspots”, i.e. manually selected FOVs. (Nassif, A. E. et al., “Immunohistochemistry expression of tumor markers cd34 and p27 as a prognostic factor of clinically localized prostate adenocarcinoma after radical prostatectomy.”, Rev. Col. Bras. Cir., October 2010, 37(5):338-344; Erovic, B. M. et al., “Quantitation of microvessel density in squamous cell carcinoma of the head and neck by computer-aided image analysis.”, Wien Klin Wochenschr, January 2005, 117(1-2): 53-57). The pitfalls associated with manual FOV selection suggest that hotspot-based predictions may not accurately represent CD34 expression in an entire slide.
Due to the high degree of heterogeneity in cancer, it is important to locate regions of interest in histopathology that are representative of the tumor as a whole. Traditional image processing involves the use of multi-scale, i.e., multi-resolution methods to extract contextual information at varying scales of a single FOV of a given image. (Doyle, S. et al., “Detecting prostatic adenocarcinoma from digitized histology using a multi-scale hierarchial classification approach.”, IEEE EMBS, 2006, 1: 4759-4762). Such multi-scale methods are useful for analysis of large images since texture provides different types of information at different resolutions. However, in such methods, object density remains invariant to changes in scale, although our visual perception and ability to detect individual objects within the image may vary. This presents technical challenges in analyzing local object density (or other localized descriptors).
The present invention circumvents the need for determining an optimal FOV size by calculating image features at multiple FOV sizes. The present invention provides methods for integrating image-based multi-parametric information from differently stained histopathology slides by using multi-field of view framework.